Endpoints
DataBridge's API is organized into several logical paths:
Endpoints for ingesting and managing documents:
Text document ingestion
File document ingestion
Document metadata management
Endpoints for semantic search functionality:
Document chunk search
Document listing and retrieval
Document metadata search
Endpoints for AI-powered query operations:
AI completions with context
RAG-based question answering
Context-aware responses
Coming soon
Common data models used across the API:
Document models
Search result models
Query response models
Response Models
Document
class Document:
external_id: str
owner: Dict[str, str]
content_type: str
filename: Optional[str]
metadata: Dict[str, Any] # user-defined metadata
storage_info: Dict[str, str] # storage backend info
system_metadata: Dict[str, Any] # creation date, version, etc.
additional_metadata: Dict[str, Any] # e.g., frame descriptions and transcripts for videos
access_control: Dict[str, List[str]] # readers, writers, admins
chunk_ids: List[str]ChunkResult
class ChunkResult:
content: str
score: float
document_id: str # external_id
chunk_number: int
metadata: Dict[str, Any]
content_type: str
filename: Optional[str]
download_url: Optional[str]
def augmented_content(self, doc: DocumentResult) -> str:
"""Get augmented content for video chunks with frame/transcript info"""DocumentResult
class DocumentResult:
score: float # Highest chunk score
document_id: str # external_id
metadata: Dict[str, Any]
content: DocumentContent # type and value fields
additional_metadata: Dict[str, Any] # e.g., frame descriptions and transcriptsDocumentContent
class DocumentContent:
type: Literal["url", "string"] # Content type
value: str # URL or actual content
filename: Optional[str] # Required for URL type, None for string typeCompletionResponse
class CompletionResponse:
completion: str
usage: TokenUsage # completion_tokens, prompt_tokens, total_tokensLast updated